An Artificial Intelligence-Based Full-Process Solution for Radiotherapy: A Proof of Concept Study on Rectal Cancer

To develop an automated treatment planning strategy for external beam intensity-modulated radiation therapy (IMRT), including a deep learning-based three-dimensional (3D) dose prediction and a dose distribution-based plan generation algorithm.

Delineation of the clinical target volume (CTV) and organs at risk (OARs) is very important for radiotherapy but is time-consuming and prone to inter-observer variation. Here, we proposed a novel deep dilated convolutional neural network (DDCNN)-based method for fast and consistent auto-segmentation of these structures.

To validate and clinically evaluate autocontouring using atlas-based autosegmentation (ABAS) of computed tomography images. The data from 10 head-and-neck patients were selected as input for ABAS, and neck levels I-V and 20 organs at risk were manually contoured according to published guidelines. The total contouring times were recorded. Two different ABAS strategies, multiple and single subject, were evaluated, and the similarity of the autocontours with the atlas contours was assessed using Dice coefficients and the mean distances, using the leave-one-out method. For 12 clinically treated patients, 5 experienced observers edited the autosegmented contours. The editing times were recorded. The Dice coefficients and mean distances were calculated among the clinically used contours, autocontours, and edited autocontours. Finally, an expert panel scored all autocontours and the edited autocontours regarding their adequacy relative to the published atlas. The time to autosegment all the structures using ABAS was 7 min/patient. No significant differences were observed in the autosegmentation accuracy for stage N0 and N+ patients. The multisubject atlas performed best, with a Dice coefficient and mean distance of 0.74 and 2 mm, 0.67 and 3 mm, 0.71 and 2 mm, 0.50 and 2 mm, and 0.78 and 2 mm for the salivary glands, neck levels, chewing muscles, swallowing muscles, and spinal cord-brainstem, respectively. The mean Dice coefficient and mean distance of the autocontours vs. the clinical contours was 0.8 and 2.4 mm for the neck levels and salivary glands, respectively. For the autocontours vs. the edited autocontours, the mean Dice coefficient and mean distance was 0.9 and 1.6 mm, respectively. The expert panel scored 100% of the autocontours as a "minor deviation, editable" or better. The expert panel scored 88% of the edited contours as good compared with 83% of the clinical contours. The total editing time was 66 min. Multiple-subject ABAS of computed tomography images proved to be a useful novel tool in the rapid delineation of target and normal tissues. Although editing of the autocontours is inevitable, a substantial time reduction was achieved using editing, instead of manual contouring (180 vs. 66 min). Copyright © 2011 Elsevier Inc. All rights reserved.